P4‐395: DEVELOPMENT OF A BEST PRACTICE GUIDELINE FOR POLICE TO USE FOR MISSING OLDER ADULTS WITH DEMENTIA
Bibliographic record
Abstract
Search and rescue of missing persons with dementia is costly, and the incidence of missing older adults with dementia has been on a rapid incline in recent years. If not found within 24 hours, up to half of individuals lost will sustain serious injury or death, a reality faced by first responders and family caregivers. Strategies, such as locator devices, offer options for finding missing persons with dementia, assuming the devices are worn. Current information describing available strategies, however, is inconsistent and is associated with a ranged of different practices between police services across jurisdictions. Therefore, the purpose of this project was to develop a guideline to promote exchange of best practices for rapid success in locating lost persons with dementia. Individual telephone interviews with paramedics, police in charge of search and rescue, and media relations were conducted. Questions ranged from strategies that have been used when working with missing persons with dementia, and what practice gaps were present in terms of existing police practices. A total of 30 first responders participated and represented Ontario, Canada. Participants described they used a wide range of high and low tech solutions. They recommended strategies that included vulnerable persons’ registries and home-based preventative strategies, such as global positioning system (GPS) devices. Gaps such as inadequate funding, lack of formal education on preventative strategies, limited relationships with local Alzheimer Societies and common misconceptions were perceived to have a significant impact on whether these practices can be successfully integrated. Information collected from the interviews were used to develop a best practice guideline for police services in partnership with the Alzheimer Society of Ontario. It is believed that this guideline will assist first responders in reducing the time it takes to find a missing person with dementia. It will link more persons with dementia at risk of getting lost to key service providers such as occupational therapists, social workers, and Alzheimer Societies. This will promote alternative preventative methods which would enable this population to continue to live in their homes of choice within their communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".